大数据技术在民航空管战术流量管理系统的应用

随着我国航空事业的快速发展,飞行流量与保障能力、空域容量之间的矛盾日渐突出。本文分析现状并提出相应的解决方案,基于大数据技术实现一套用于民航空管战术流量管理的信息系统。该系统能够帮助空管决策者在流量管理战术阶段调整航班的起飞策略从而平衡空域的流量和容量,避免空中交通拥塞,减少航班地面等待的时间,实现空域资源的有效利用,最终提高空管单位的管制服务质量及航班旅客的出行体验。

Stochastic Air Traffic Flow Management for Demand and Capacity Balancing Under Capacity Uncertainty

This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)framework.Further with previous study,the uncertainty in capacity is considered as a non-negligible issue regarding multiple reasons,like the impact of weather,the strike of air traffic controllers(ATCOs),the military use of airspace and the spatiotemporal distribution of nonscheduled flights,etc.These recessive factors affect the outcome of traffic flow optimization.In this research,the focus is placed on the impact of sector capacity uncertainty on demand and capacity balancing(DCB)optimization and ATFM,and multiple options,such as delay assignment and rerouting,are intended for regulating the traffic flow.A scenario optimization method for sector capacity in the presence of uncertainties is used to find the approximately optimal solution.The results show that the proposed approach can achieve better demand and capacity balancing and determine perfect integer solutions to ATFM problems,solving large-scale instances(24 h on seven capacity scenarios,with 6255 flights and 8949 trajectories)in 5-15 min.To the best of our knowledge,our experiment is the first to tackle large-scale instances of stochastic ATFM problems within the collaborative ATFM framework.

FLIGHT CONFLICT DETECTION AND RESOLUTION

The air traffic control (ATC) systems are facing more and more serious congestive because of the increasing of air traffic flow in China. One of the most available ways to solve the problem is 'free flight' that the pilots may choose the air route and flight speed suitable for them. But this will lead to the difficulties for the controllers. This paper presents how ATC genetic algorithms can be used to detect and to solve air traffic control conflicts in free flight. And it also shows that this algorithm perfectly suits for solving flight conflicts resolution because of its short computing time.

一种基于雷达数据融合的航班4D航迹预测方法

4D航迹预测是空管自动化系统及流量管理系统的一项核心技术,能够提高空域的利用率和安全性。本文提出了一种4D航迹预测方法,结合多数据源雷达、气象、航班计划、航空器性能等数据,利用飞行经验样本推测各时间点的航空器位置,从而得出航空器的飞行轨迹与时间的精准预测。通过在华东空管流量管理系统及空管自动化系统中的应用效果,说明该方法的实用性和有效性。

Terminal Traffic Flow Prediction Method Under Convective Weather Using Deep Learning Approaches

In order to improve the accuracy and stability of terminal traffic flow prediction in convective weather,a multi-input deep learning(MICL)model is proposed.On the basis of previous studies,this paper expands the set of weather characteristics affecting the traffic flow in the terminal area,including weather forecast data and Meteorological Report of Aerodrome Conditions(METAR)data.The terminal airspace is divided into smaller areas based on function and the weather severity index(WSI)characteristics extracted from weather forecast data are established to better quantify the impact of weather.MICL model preserves the advantages of the convolution neural network(CNN)and the long short-term memory(LSTM)model,and adopts two channels to input WSI and METAR information,respectively,which can fully reflect the temporal and spatial distribution characteristics of weather in the terminal area.Multi-scene experiments are designed based on the real historical data of Guangzhou Terminal Area operating in typical convective weather.The results show that the MICL model has excellent performance in mean squared error(MSE),root MSE(RMSE),mean absolute error(MAE)and other performance indicators compared with the existing machine learning models or deep learning models,such as Knearest neighbor(KNN),support vector regression(SVR),CNN and LSTM.In the forecast period ranging from 30 min to 6 h,the MICL model has the best prediction accuracy and stability.

Study on the heat transfer of cross flow in vertical upward tubes

A special device was designed to measure temperature difference in this study of heat transfer of water and oil cross flow inside vertical upward tubes. A new heat transfer correlation was obtained for cross flow. The experimental results showed that the dependence of heat transfer on Reynolds is much smaller in a narrow space than that in a wide space. It was found that the heat transfer correlation of cross flow in a narrow space is obviously different from that in a wide space, and that the heat transfer correlation obtained in a wide space may not be applicable to the cross-flow heat transfer in a narrow space. Further, the single-phase heat transfer capability of water cross flow was compared with that of oil cross flow. The experimental results showed that the average heat transfer coefficient of water is about 2～3 times that ofoil when they have the same superficial velocity.

Thermal Modeling and Cooling Analysis of High-power Lithium Ion Cells

The heat generation model and three-dimensional computational fluid dynamics model for lithium ion cells were established with boundary conditions defined.In order to provide a better insight about the behaviors of high-power lithium ion cells under realistic discharge conditions,the temperature difference of the cells and an active thermal management system with a pure air-cooling mode were analyzed and predicted with the factors affecting the unevenness of temperature field discussed.The results show a significant effect of the cooling flow rate on the temperature rise of the cells for all discharge rates.Average surface temperatures are relatively uniform at lower discharge rate that makes it easier to control the temperature of the pack.Cell temperatures are expected to rise significantly toward the end of discharge and they show non-uniformity at higher discharge rates.Adequate air flow rate of active cooling is required at high discharge rate and high ambient temperature for practical pack thermal management system.

Numerical and experimental investigations for an air cannon optimization

Air cannon is a kind of de-clogging device which produces impulse force by instantly releasing the compressed air deposited in a pressure vessel. Air cannons are widely used in the transport pipes of warehouses, docks, furnaces and coal mines. In this paper, the theoretical analysis with isentropic flow hypothesis is firstly conducted on a simplified mode/ to deduce the theo- retical maximum of impulse force. And numerical study is carried out to predict the steady and unsteady impulse forces via simulating the whole exhausting process of the air cannon. The results demonstrate that the impulse force can be improved via increasing the piston sleeve inlet length and increasing the nozzle diameter. Laval nozzle can also increase the impulse force of the air without increasing the air mass flow. The optimization of the air cannon is then conducted on the basis of the theoretical and numerical analyses. Experimental measurements indicate that the computations well simulate the working process of the air cannon and the impulse force of the optimized design is 50% higher than the original model. For the cases with working pressure of 0.8 MPa, the optimized design is 60% higher than the original one.